Subendhu Rongali, Mukund Sridhar, Haidar Khan, Konstantine Arkoudas, Wael Hamza, Andrew McCallum. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Recognizing the intents and domains of users’ spoken and written language is a key component of Natural Language Understanding (NLU) systems. Real applications however encounter dynamic, rapidly evolving environments with newly emerging intents and domains, for which no labeled data or prior information is available. For such a setting, we propose a novel framework, ADVIN, to automatically discover novel domains and intents from large volumes of unlabeled text. We first employ an open classification model to discriminate all utterances potentially consisting of a novel intent. Next, we train a deep learning model with a pairwise margin loss function and knowledge transfer, to discover multiple latent intent categories in an unsupervised manner. We finally form a hierarchical intent-domain taxonomy by linking mutually related novel intents into novel domains. ADVIN significantly outperforms strong baselines on four benchmark datasets, and data from a real-world voice agent.

We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system. Though we train using 70% spoken-form data, our teacher models perform comparably to XLM-R and mT5 when evaluated on the written-form Cross-lingual Natural Language Inference (XNLI) corpus. We perform a second stage of pretraining on our teacher models using in-domain data from our system, improving error rates by 3.86% relative for intent classification and 7.01% relative for slot filling. We find that even a 170M-parameter model distilled from our Stage 2 teacher model has 2.88% better intent classification and 7.69% better slot filling error rates when compared to the 2.3B-parameter teacher trained only on public data (Stage 1), emphasizing the importance of in-domain data for pretraining. When evaluated offline using labeled NLU data, our 17M-parameter Stage 2 distilled model outperforms both XLM-R Base (85M params) and DistillBERT (42M params) by 4.23% to 6.14%, respectively. Finally, we present results from a full virtual assistant experimentation platform, where we find that models trained using our pretraining and distillation pipeline outperform models distilled from 85M-parameter teachers by 3.74%-4.91% on an automatic measurement of full-system user dissatisfaction.

Shuyang Li, Jin Cao, Mukund Sridhar, Henghui Zhu, Shang-Wen Li, Wael Hamza, Julian McAuley. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.